If the agent continually learns to classify a new set of unseen classes, this particular problem is named class-incremental OCL. It is one of the most prevalent settings in the community of OCL. However, learning on unseen classes would change the model parameters (and the feature representation space) optimised for the old classes. Hence, the model classification accuracy on the old classes inevitably deteriorates. This phenomenon is well-known and called Catastrophic Forgetting [1]}.
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